A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.
This report has been generated by the nf-core/sarek analysis pipeline. For information about how to interpret these results, please see the documentation.
Report
generated on 2025-02-18, 22:04 CET
based on data in:
/vulpes/proj/ngis/ngi2016004/private/strategic_proj/SR_23_02_Element_vs_Illumina/analysis/nfcore_sarek_rerun
General Statistics
| Sample Name | % Duplication | M Reads After Filtering | GC content | % PF | Duplication | Error rate | Non-primary | Reads mapped | % Mapped | % Proper pairs | Total seqs | ≥ 30X | Median | Mean Cov. | Vars | SNP | Indel | Ts/Tv |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| aviti_hq | KMS12BM-20231208_PLT-04_EBSL-0477-OBPA_KMS-812-5mil-DE090-2A1A-1 | 1.3% | 216.0M | 41.1% | 100.0% | ||||||||||||||
| aviti_hq | KMS12BM-20231208_PLT-04_EBSL-0477-OBPA_KMS-812-5mil-DE090-2A1A-2 | 1.0% | 167.1M | 41.2% | 100.0% | ||||||||||||||
| aviti_hq | KMS12BM-20231208_PLT-04_EBSL-0477-OBPA_KMS-812-5mil-DE090-2A1A-3 | 0.9% | 157.1M | 41.2% | 100.0% | ||||||||||||||
| aviti_hq | KMS12BM-20231208_PLT-04_EBSL-0477-OBPA_KMS-812-5mil-DE090-2A1A-3.KMS12BM-20231208_PLT-04_EBSL-0477-OBPA_KMS-812-5mil-DE090-2A1A-3_1 | 1.0% | |||||||||||||||||
| aviti_hq | KMS12BM-20231208_PLT-04_EBSL-0478-OBPA_KMS-812-5mil-DE090-2A1A-1 | 1.3% | 214.9M | 41.2% | 100.0% | ||||||||||||||
| aviti_hq | KMS12BM-20231208_PLT-04_EBSL-0478-OBPA_KMS-812-5mil-DE090-2A1A-2 | 1.0% | 166.4M | 41.2% | 100.0% | ||||||||||||||
| aviti_hq | KMS12BM-20231208_PLT-04_EBSL-0478-OBPA_KMS-812-5mil-DE090-2A1A-3 | 0.9% | 156.2M | 41.2% | 100.0% | ||||||||||||||
| aviti_hq | KMS12BM.deepvariant | 6032754 | 4932612 | 1104333 | 1.45 | ||||||||||||||
| aviti_hq | KMS12BM.md | 0.45% | 0.0M | 1076.3M | 99.9% | 98.6% | 1077.7M | 82.0% | 52.0X | 51.4 | |||||||||
| aviti_hq | MM1S-20231208_PLT-04_EBSL-0477-OBPA_MM1-S-5mil-DE090-3A1A-1 | 1.0% | 168.1M | 41.0% | 100.0% | ||||||||||||||
| aviti_hq | MM1S-20231208_PLT-04_EBSL-0477-OBPA_MM1-S-5mil-DE090-3A1A-2 | 1.0% | 172.6M | 41.0% | 100.0% | ||||||||||||||
| aviti_hq | MM1S-20231208_PLT-04_EBSL-0477-OBPA_MM1-S-5mil-DE090-3A1A-3 | 1.1% | 185.9M | 40.9% | 100.0% | ||||||||||||||
| aviti_hq | MM1S-20231208_PLT-04_EBSL-0478-OBPA_MM1-S-5mil-DE090-3A1A-1 | 1.0% | 167.3M | 41.0% | 100.0% | ||||||||||||||
| aviti_hq | MM1S-20231208_PLT-04_EBSL-0478-OBPA_MM1-S-5mil-DE090-3A1A-2 | 1.0% | 171.8M | 41.0% | 100.0% | ||||||||||||||
| aviti_hq | MM1S-20231208_PLT-04_EBSL-0478-OBPA_MM1-S-5mil-DE090-3A1A-2.MM1S-20231208_PLT-04_EBSL-0478-OBPA_MM1-S-5mil-DE090-3A1A-2_1 | 1.4% | |||||||||||||||||
| aviti_hq | MM1S-20231208_PLT-04_EBSL-0478-OBPA_MM1-S-5mil-DE090-3A1A-3 | 1.1% | 185.0M | 40.9% | 100.0% | ||||||||||||||
| aviti_hq | MM1S.deepvariant | 7310096 | 5972578 | 1345142 | 1.52 | ||||||||||||||
| aviti_hq | MM1S.md | 0.49% | 0.0M | 1049.3M | 99.9% | 98.5% | 1050.7M | 84.0% | 52.0X | 49.9 | |||||||||
| aviti_hq | OPM2-20231208_PLT-04_EBSL-0477-OBPA_5mil-OPM2-DE090-1A1A-1 | 1.0% | 168.0M | 41.0% | 100.0% | ||||||||||||||
| aviti_hq | OPM2-20231208_PLT-04_EBSL-0477-OBPA_5mil-OPM2-DE090-1A1A-2 | 1.2% | 207.5M | 41.0% | 100.0% | ||||||||||||||
| aviti_hq | OPM2-20231208_PLT-04_EBSL-0477-OBPA_5mil-OPM2-DE090-1A1A-3 | 0.9% | 152.0M | 40.9% | 100.0% | ||||||||||||||
| aviti_hq | OPM2-20231208_PLT-04_EBSL-0478-OBPA_5mil-OPM2-DE090-1A1A-1 | 1.0% | 167.3M | 41.0% | 100.0% | ||||||||||||||
| aviti_hq | OPM2-20231208_PLT-04_EBSL-0478-OBPA_5mil-OPM2-DE090-1A1A-2 | 1.2% | 206.2M | 41.0% | 100.0% | ||||||||||||||
| aviti_hq | OPM2-20231208_PLT-04_EBSL-0478-OBPA_5mil-OPM2-DE090-1A1A-3 | 0.9% | 151.4M | 41.0% | 100.0% | ||||||||||||||
| aviti_hq | OPM2-20231208_PLT-04_EBSL-0478-OBPA_5mil-OPM2-DE090-1A1A-3.OPM2-20231208_PLT-04_EBSL-0478-OBPA_5mil-OPM2-DE090-1A1A-3_1 | 1.2% | |||||||||||||||||
| aviti_hq | OPM2.deepvariant | 6258119 | 5100554 | 1162788 | 1.46 | ||||||||||||||
| aviti_hq | OPM2.md | 0.46% | 0.0M | 1051.2M | 99.9% | 98.7% | 1052.4M | 87.0% | 49.0X | 50.1 | |||||||||
| aviti_hq | REH-20231208_PLT-04_EBSL-0477-OBPA_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-1 | 0.9% | 158.0M | 41.0% | 100.0% | ||||||||||||||
| aviti_hq | REH-20231208_PLT-04_EBSL-0477-OBPA_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-2 | 0.9% | 152.4M | 41.0% | 100.0% | ||||||||||||||
| aviti_hq | REH-20231208_PLT-04_EBSL-0477-OBPA_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-3 | 1.0% | 166.2M | 41.0% | 100.0% | ||||||||||||||
| aviti_hq | REH-20231208_PLT-04_EBSL-0478-OBPA_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-1 | 0.9% | 156.7M | 41.1% | 100.0% | ||||||||||||||
| aviti_hq | REH-20231208_PLT-04_EBSL-0478-OBPA_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-1.REH-20231208_PLT-04_EBSL-0478-OBPA_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-1_1 | 1.1% | |||||||||||||||||
| aviti_hq | REH-20231208_PLT-04_EBSL-0478-OBPA_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-2 | 0.9% | 151.2M | 41.0% | 100.0% | ||||||||||||||
| aviti_hq | REH-20231208_PLT-04_EBSL-0478-OBPA_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-3 | 1.0% | 165.0M | 41.0% | 100.0% | ||||||||||||||
| aviti_hq | REH.deepvariant | 7318664 | 5547147 | 1781287 | 1.52 | ||||||||||||||
| aviti_hq | REH.md | 0.51% | 0.0M | 948.3M | 99.9% | 98.4% | 949.6M | 86.0% | 47.0X | 45.3 | |||||||||
| aviti_ngi | KMS12BM-B2403418434_KMS-812-5mil-DE090-2A1A-1 | 1.0% | 145.5M | 41.3% | 100.0% | ||||||||||||||
| aviti_ngi | KMS12BM-B2403418435_KMS-812-5mil-DE090-2A1A-2 | 0.9% | 122.2M | 41.4% | 100.0% | ||||||||||||||
| aviti_ngi | KMS12BM-B2403418436_KMS-812-5mil-DE090-2A1A-3 | 0.8% | 112.6M | 41.4% | 100.0% | ||||||||||||||
| aviti_ngi | KMS12BM-B2403418436_KMS-812-5mil-DE090-2A1A-3.KMS12BM-B2403418436_KMS-812-5mil-DE090-2A1A-3_1 | 3.4% | |||||||||||||||||
| aviti_ngi | KMS12BM.deepvariant | 5472867 | 4503819 | 972799 | 1.55 | ||||||||||||||
| aviti_ngi | KMS12BM.md | 0.38% | 0.0M | 379.7M | 99.9% | 98.6% | 380.3M | 7.0% | 17.0X | 17.7 | |||||||||
| aviti_ngi | MM1S-B2403418437_MM1-S-5mil-DE090-3A1A-1 | 0.8% | 122.2M | 41.1% | 100.0% | ||||||||||||||
| aviti_ngi | MM1S-B2403418437_MM1-S-5mil-DE090-3A1A-1.MM1S-B2403418437_MM1-S-5mil-DE090-3A1A-1_1 | 3.7% | |||||||||||||||||
| aviti_ngi | MM1S-B2403418438_MM1-S-5mil-DE090-3A1A-2 | 0.9% | 124.9M | 41.1% | 100.0% | ||||||||||||||
| aviti_ngi | MM1S-B2403418439_MM1-S-5mil-DE090-3A1A-3 | 0.9% | 135.3M | 41.1% | 100.0% | ||||||||||||||
| aviti_ngi | MM1S.deepvariant | 6699677 | 5519015 | 1187308 | 1.62 | ||||||||||||||
| aviti_ngi | MM1S.md | 0.42% | 0.0M | 381.8M | 99.9% | 98.5% | 382.4M | 4.0% | 18.0X | 17.7 | |||||||||
| aviti_ngi | OPM2-B2403418431_5mil-OPM2-DE090-1A1A-1 | 1.0% | 143.6M | 41.2% | 100.0% | ||||||||||||||
| aviti_ngi | OPM2-B2403418431_5mil-OPM2-DE090-1A1A-1.OPM2-B2403418431_5mil-OPM2-DE090-1A1A-1_1 | 3.6% | |||||||||||||||||
| aviti_ngi | OPM2-B2403418432_5mil-OPM2-DE090-1A1A-2 | 0.9% | 146.7M | 41.2% | 100.0% | ||||||||||||||
| aviti_ngi | OPM2-B2403418433_5mil-OPM2-DE090-1A1A-3 | 1.0% | 140.9M | 41.2% | 100.0% | ||||||||||||||
| aviti_ngi | OPM2.deepvariant | 5741130 | 4712295 | 1033437 | 1.55 | ||||||||||||||
| aviti_ngi | OPM2.md | 0.37% | 0.0M | 430.7M | 99.9% | 98.7% | 431.2M | 9.0% | 20.0X | 20.0 | |||||||||
| aviti_ngi | REH-B2403418440_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-1 | 0.7% | 103.8M | 41.3% | 100.0% | ||||||||||||||
| aviti_ngi | REH-B2403418440_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-1.REH-B2403418440_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-1_1 | 3.2% | |||||||||||||||||
| aviti_ngi | REH-B2403418441_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-2 | 0.8% | 115.5M | 41.2% | 100.0% | ||||||||||||||
| aviti_ngi | REH-B2403418442_REH-wb-8-0-milj-1211190-P7-DE056-4A1A-3 | 0.7% | 107.1M | 41.3% | 100.0% | ||||||||||||||
| aviti_ngi | REH.deepvariant | 6541810 | 5068647 | 1481122 | 1.63 | ||||||||||||||
| aviti_ngi | REH.md | 0.43% | 0.0M | 325.9M | 99.8% | 98.3% | 326.4M | 1.0% | 15.0X | 15.2 | |||||||||
| xplus_sns | KMS12BM-L001_Sample_FU-199-KMS-812-5mil-DE090-2A1A | 18.7% | 260.4M | 41.0% | 100.0% | ||||||||||||||
| xplus_sns | KMS12BM-L001_Sample_FU-199-KMS-812-5mil-DE090-2A1A_1 | 19.0% | 244.0M | 41.1% | 100.0% | ||||||||||||||
| xplus_sns | KMS12BM-L001_Sample_FU-199-KMS-812-5mil-DE090-2A1A_2 | 18.5% | 203.3M | 41.1% | 100.0% | ||||||||||||||
| xplus_sns | KMS12BM-L002_Sample_FU-199-KMS-812-5mil-DE090-2A1A | 7.1% | 249.8M | 40.9% | 100.0% | ||||||||||||||
| xplus_sns | KMS12BM-L002_Sample_FU-199-KMS-812-5mil-DE090-2A1A_1 | 7.2% | 234.7M | 40.9% | 100.0% | ||||||||||||||
| xplus_sns | KMS12BM-L002_Sample_FU-199-KMS-812-5mil-DE090-2A1A_2 | 6.8% | 196.6M | 40.9% | 100.0% | ||||||||||||||
| xplus_sns | KMS12BM-L002_Sample_FU-199-KMS-812-5mil-DE090-2A1A_2.KMS12BM-L002_Sample_FU-199-KMS-812-5mil-DE090-2A1A_2_1 | 21.3% | |||||||||||||||||
| xplus_sns | KMS12BM.deepvariant | 7153111 | 6014299 | 1142866 | 1.44 | ||||||||||||||
| xplus_sns | KMS12BM.md | 0.49% | 0.0M | 1384.5M | 99.7% | 97.9% | 1388.8M | 83.0% | 53.0X | 52.4 | |||||||||
| xplus_sns | MM1S-L001_Sample_FU-199-MM1-S-5mil-DE090-3A1A | 19.7% | 231.4M | 40.9% | 100.0% | ||||||||||||||
| xplus_sns | MM1S-L001_Sample_FU-199-MM1-S-5mil-DE090-3A1A.MM1S-L001_Sample_FU-199-MM1-S-5mil-DE090-3A1A_1 | 22.6% | |||||||||||||||||
| xplus_sns | MM1S-L001_Sample_FU-199-MM1-S-5mil-DE090-3A1A_1 | 17.7% | 220.0M | 40.9% | 100.0% | ||||||||||||||
| xplus_sns | MM1S-L001_Sample_FU-199-MM1-S-5mil-DE090-3A1A_2 | 20.8% | 255.1M | 40.9% | 100.0% | ||||||||||||||
| xplus_sns | MM1S-L002_Sample_FU-199-MM1-S-5mil-DE090-3A1A | 7.4% | 222.6M | 40.7% | 100.0% | ||||||||||||||
| xplus_sns | MM1S-L002_Sample_FU-199-MM1-S-5mil-DE090-3A1A_1 | 6.5% | 210.2M | 40.7% | 100.0% | ||||||||||||||
| xplus_sns | MM1S-L002_Sample_FU-199-MM1-S-5mil-DE090-3A1A_2 | 7.9% | 235.2M | 40.7% | 100.0% | ||||||||||||||
| xplus_sns | MM1S.deepvariant | 8386268 | 7025410 | 1368178 | 1.52 | ||||||||||||||
| xplus_sns | MM1S.md | 0.54% | 0.0M | 1370.9M | 99.7% | 97.8% | 1374.5M | 85.0% | 52.0X | 51.0 | |||||||||
| xplus_sns | OPM2-L001_Sample_FU-199-5mil-OPM2-DE090-1A1A | 21.4% | 282.0M | 41.0% | 100.0% | ||||||||||||||
| xplus_sns | OPM2-L001_Sample_FU-199-5mil-OPM2-DE090-1A1A_1 | 20.1% | 276.2M | 40.9% | 100.0% | ||||||||||||||
| xplus_sns | OPM2-L001_Sample_FU-199-5mil-OPM2-DE090-1A1A_2 | 18.7% | 243.0M | 41.0% | 100.0% | ||||||||||||||
| xplus_sns | OPM2-L002_Sample_FU-199-5mil-OPM2-DE090-1A1A | 8.4% | 272.1M | 40.8% | 100.0% | ||||||||||||||
| xplus_sns | OPM2-L002_Sample_FU-199-5mil-OPM2-DE090-1A1A_1 | 7.7% | 264.3M | 40.8% | 100.0% | ||||||||||||||
| xplus_sns | OPM2-L002_Sample_FU-199-5mil-OPM2-DE090-1A1A_2 | 7.1% | 236.1M | 40.8% | 100.0% | ||||||||||||||
| xplus_sns | OPM2-L002_Sample_FU-199-5mil-OPM2-DE090-1A1A_2.OPM2-L002_Sample_FU-199-5mil-OPM2-DE090-1A1A_2_1 | 22.6% | |||||||||||||||||
| xplus_sns | OPM2.deepvariant | 7310984 | 6119230 | 1196491 | 1.44 | ||||||||||||||
| xplus_sns | OPM2.md | 0.48% | 0.0M | 1569.5M | 99.7% | 98.1% | 1573.8M | 91.0% | 57.0X | 58.4 | |||||||||
| xplus_sns | REH-L001_Sample_FU-199-REH-wb-8-0-milj-1211190-P7-DE056-4A1A-1 | 19.6% | 207.4M | 41.0% | 100.0% | ||||||||||||||
| xplus_sns | REH-L001_Sample_FU-199-REH-wb-8-0-milj-1211190-P7-DE056-4A1A-2 | 22.0% | 246.2M | 41.0% | 100.0% | ||||||||||||||
| xplus_sns | REH-L001_Sample_FU-199-REH-wb-8-0-milj-1211190-P7-DE056-4A1A-3 | 20.1% | 213.1M | 41.0% | 100.0% | ||||||||||||||
| xplus_sns | REH-L002_Sample_FU-199-REH-wb-8-0-milj-1211190-P7-DE056-4A1A-1 | 7.2% | 190.5M | 40.8% | 100.0% | ||||||||||||||
| xplus_sns | REH-L002_Sample_FU-199-REH-wb-8-0-milj-1211190-P7-DE056-4A1A-1.REH-L002_Sample_FU-199-REH-wb-8-0-milj-1211190-P7-DE056-4A1A-1_1 | 24.0% | |||||||||||||||||
| xplus_sns | REH-L002_Sample_FU-199-REH-wb-8-0-milj-1211190-P7-DE056-4A1A-2 | 8.4% | 227.2M | 40.7% | 100.0% | ||||||||||||||
| xplus_sns | REH-L002_Sample_FU-199-REH-wb-8-0-milj-1211190-P7-DE056-4A1A-3 | 7.4% | 197.1M | 40.8% | 100.0% | ||||||||||||||
| xplus_sns | REH.deepvariant | 8055071 | 6266749 | 1797390 | 1.57 | ||||||||||||||
| xplus_sns | REH.md | 0.54% | 0.0M | 1277.9M | 99.7% | 97.6% | 1281.6M | 87.0% | 48.0X | 46.7 |
FastP (Read preprocessing)
FastP (Read preprocessing) An ultra-fast all-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...).DOI: 10.1093/bioinformatics/bty560.
Filtered Reads
Filtering statistics of sampled reads.
Insert Sizes
Insert size estimation of sampled reads.
Sequence Quality
Average sequencing quality over each base of all reads.
GC Content
Average GC content over each base of all reads.
N content
Average N content over each base of all reads.
GATK4 MarkDuplicates
GATK4 MarkDuplicates metrics generated either by GATK4 MarkDuplicates or EstimateLibraryComplexity (with --use_gatk_spark).
Mark Duplicates
Number of reads, categorised by duplication state. Pair counts are doubled - see help text for details.
The table in the Picard metrics file contains some columns referring read pairs and some referring to single reads.
To make the numbers in this plot sum correctly, values referring to pairs are doubled according to the scheme below:
READS_IN_DUPLICATE_PAIRS = 2 * READ_PAIR_DUPLICATESREADS_IN_UNIQUE_PAIRS = 2 * (READ_PAIRS_EXAMINED - READ_PAIR_DUPLICATES)READS_IN_UNIQUE_UNPAIRED = UNPAIRED_READS_EXAMINED - UNPAIRED_READ_DUPLICATESREADS_IN_DUPLICATE_PAIRS_OPTICAL = 2 * READ_PAIR_OPTICAL_DUPLICATESREADS_IN_DUPLICATE_PAIRS_NONOPTICAL = READS_IN_DUPLICATE_PAIRS - READS_IN_DUPLICATE_PAIRS_OPTICALREADS_IN_DUPLICATE_UNPAIRED = UNPAIRED_READ_DUPLICATESREADS_UNMAPPED = UNMAPPED_READS
Samtools Flagstat
Samtools is a suite of programs for interacting with high-throughput sequencing data.DOI: 10.1093/bioinformatics/btp352.
Percent mapped
Alignment metrics from samtools stats; mapped vs. unmapped reads vs. reads mapped with MQ0.
For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.
Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).
Reads mapped with MQ0 often indicate that the reads are ambiguously mapped to multiple locations in the reference sequence. This can be due to repetitive regions in the genome, the presence of alternative contigs in the reference, or due to reads that are too short to be uniquely mapped. These reads are often filtered out in downstream analyses.
Alignment stats
This module parses the output from samtools stats. All numbers in millions.
Mosdepth
Mosdepth performs fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing.DOI: 10.1093/bioinformatics/btx699.
Cumulative coverage distribution
Proportion of bases in the reference genome with, at least, a given depth of coverage. Note that for 12 samples, a BED file was provided, so the data was calculated across those regions. For 12 samples, it's calculated across the entire genome length. 12 samples have both global and region reports, and we are showing the data for regions
For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).
Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).
For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.
Coverage distribution
Proportion of bases in the reference genome with a given depth of coverage. Note that for 12 samples, a BED file was provided, so the data was calculated across those regions. For 12 samples, it's calculated across the entire genome length. 12 samples have both global and region reports, and we are showing the data for regions
For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position (Sims et al. 2014).
Bases of a reference sequence (y-axis) are groupped by their depth of coverage (0×, 1×, …, N×) (x-axis). This plot shows the frequency of coverage depths relative to the reference sequence for each read dataset, which provides an indirect measure of the level and variation of coverage depth in the corresponding sequenced sample.
If reads are randomly distributed across the reference sequence, this plot should resemble a Poisson distribution (Lander & Waterman 1988), with a peak indicating approximate depth of coverage, and more uniform coverage depth being reflected in a narrower spread. The optimal level of coverage depth depends on the aims of the experiment, though it should at minimum be sufficiently high to adequately address the biological question; greater uniformity of coverage is generally desirable, because it increases breadth of coverage for a given depth of coverage, allowing equivalent results to be achieved at a lower sequencing depth (Sampson et al. 2011; Sims et al. 2014). However, it is difficult to achieve uniform coverage depth in practice, due to biases introduced during sample preparation (van Dijk et al. 2014), sequencing (Ross et al. 2013) and read mapping (Sims et al. 2014).
This plot may include a small peak for regions of the reference sequence with zero depth of coverage. Such regions may be absent from the given sample (due to a deletion or structural rearrangement), present in the sample but not successfully sequenced (due to bias in sequencing or preparation), or sequenced but not successfully mapped to the reference (due to the choice of mapping algorithm, the presence of repeat sequences, or mismatches caused by variants or sequencing errors). Related factors cause most datasets to contain some unmapped reads (Sims et al. 2014).
Average coverage per contig
Average coverage per contig or chromosome
XY coverage
Bcftools
Bcftools contains utilities for variant calling and manipulating VCFs and BCFs.DOI: 10.1093/gigascience/giab008.
Variant Substitution Types
Variant Quality
Indel Distribution
Vcftools
Vcftools is a program for working with and reporting on VCF files.DOI: 10.1093/bioinformatics/btr330.
TsTv by Count
Plot of TSTV-BY-COUNT - the transition to transversion ratio as a function of alternative allele count from the output of vcftools TsTv-by-count.
Transition is a purine-to-purine or pyrimidine-to-pyrimidine point mutations.
Transversion is a purine-to-pyrimidine or pyrimidine-to-purine point mutation.
Alternative allele count is the number of alternative alleles at the site.
Note: only bi-allelic SNPs are used (multi-allelic sites and INDELs are skipped.)
Refer to Vcftools's manual (https://vcftools.github.io/man_latest.html) on --TsTv-by-count
TsTv by Qual
Plot of TSTV-BY-QUAL - the transition to transversion ratio as a function of SNP quality from the output of vcftools TsTv-by-qual.
Transition is a purine-to-purine or pyrimidine-to-pyrimidine point mutations.
Transversion is a purine-to-pyrimidine or pyrimidine-to-purine point mutation.
Quality here is the Phred-scaled quality score as given in the QUAL column of VCF.
Note: only bi-allelic SNPs are used (multi-allelic sites and INDELs are skipped.)
Refer to Vcftools's manual (https://vcftools.github.io/man_latest.html) on --TsTv-by-qual
Software Versions
Software Versions lists versions of software tools extracted from file contents.
| Group | Software | Version |
|---|---|---|
| BCFTOOLS_STATS | bcftools | 1.18 |
| BWAMEM1_MEM | bwa | 0.7.17.post1188 |
| samtools | 1.19.2 | |
| DEEPVARIANT | deepvariant | 1.5.0 |
| FASTP | fastp | 0.23.4 |
| GATK4 MarkDuplicates | gatk4 | 4.5.0.0 |
| samtools | 1.19.2 | |
| MERGE_DEEPVARIANT_GVCF | gatk4 | 4.5.0.0 |
| MERGE_DEEPVARIANT_VCF | gatk4 | 4.5.0.0 |
| Mosdepth | mosdepth | 0.3.8 |
| SAMTOOLS_STATS | samtools | 1.19.2 |
| VCFTOOLS_TSTV_COUNT | vcftools | 0.1.16 |
| Workflow | Nextflow | 24.4.2 |
| nf-core/sarek | 3.4.2 |
nf-core/sarek Methods Description
Suggested text and references to use when describing pipeline usage within the methods section of a publication.
Methods
Data was processed using nf-core/sarek v3.4.2 (doi: 10.12688/f1000research.16665.2), (doi: 10.1093/nargab/lqae031), (doi: 10.5281/zenodo.3476425) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.
The pipeline was executed with Nextflow v24.04.1 (Di Tommaso et al., 2017) with the following command:
nextflow run /vulpes/ngi/production/v24.07/sw/sarek/3_4_2/ -profile uppmax --project ngi2016004 -c /vulpes/ngi/production/v24.07/conf/sarek_sthlm.config -c ../nextflow.config -params-file aviti_hq_params.yaml -resume
References
- Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
- Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
- Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
- da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
Notes:
- The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
- You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.
nf-core/sarek Workflow Summary
- this information is collected when the pipeline is started.
Core Nextflow options
- runName
- tender_venter
- containerEngine
- singularity
- launchDir
- /vulpes/proj/ngis/ngi2016004/private/strategic_proj/SR_23_02_Element_vs_Illumina/analysis/nfcore_sarek_rerun/aviti_hq
- workDir
- /vulpes/proj/ngis/ngi2016004/private/strategic_proj/SR_23_02_Element_vs_Illumina/analysis/nfcore_sarek_rerun/aviti_hq/work
- projectDir
- /vulpes/ngi/production/v24.07/sw/sarek/3_4_2
- userName
- phojer
- profile
- uppmax
- configFiles
- N/A
Input/output options
- input
- aviti_hq_samples.csv
- outdir
- outdir
Main options
- tools
- deepvariant,cnvkit
- skip_tools
- baserecalibrator,fastqc
FASTQ Preprocessing
- trim_fastq
- true
Reference genome options
- bwa
- /vulpes/proj/ngis/ngi2016004/private/strategic_proj/SR_23_02_Element_vs_Illumina/resources/GRCh38_GIABv3/unbgzipped/
- fasta
- /vulpes/proj/ngis/ngi2016004/private/strategic_proj/SR_23_02_Element_vs_Illumina/resources/GRCh38_GIABv3/unbgzipped/genome.fa
- fasta_fai
- /vulpes/proj/ngis/ngi2016004/private/strategic_proj/SR_23_02_Element_vs_Illumina/resources/GRCh38_GIABv3/unbgzipped/genome.fa.fai
- save_reference
- true
- igenomes_base
- /sw/data/igenomes/
- igenomes_ignore
- true
Institutional config options
- custom_config_base
- /vulpes/ngi/production/v24.07/sw/sarek/3_4_2/../configs/
- config_profile_description
- nf-core/sarek uppmax profile provided by nf-core/configs
- config_profile_contact
- Maxime Garcia (@MaxUlysse)
- config_profile_url
- https://www.uppmax.uu.se/
- seq_center
- Element_Biosciences_San_Diego
- seq_platform
- ELEMENT
Max job request options
- max_cpus
- 48
- max_memory
- 357 GB
- max_time
- 20d
Generic options
- pontus.hojer@scilifelab.se
- validationLenientMode
- true